Robot Learning Engineer (m/f/d) VLA & Foundation Models

The AI Teams at Agile Robots are looking for a Robot Learning Engineer (m/f/d) in VLA & Foundation Models , who will develop and deploy vision- and language-conditioned robot policies for real-world manipulation on physical robot platforms. Your Responsibilities Policy Development: Design, train, and evaluate vision- and language-conditioned robot policies using approaches such as imitation learning, diffusion models, transformer architectures, or reinforcement learning. Data Pipelines: Build workflows for collecting, structuring, and validating multimodal robot data from teleoperation, simulation, and real-world experiments, covering video, proprioception, force signals, and task outcomes. Robot Integration: Define action spaces, control interfaces, temporal chunking, and evaluation protocols to ensure learned policies are compatible with real robot execution contexts. Failure Analysis: Test policies in simulation and on physical robots, diagnosing failures caused by distribution shift, perception errors, contact dynamics, latency, and data quality. Deployment: Integrate learned policies into robot software stacks, with attention to latency, synchronization, safety checks, fallback behavior, and runtime monitoring. Research Application: Track developments in robot learning, VLA models, and foundation model methods, applying relevant advances to Agile's robot platforms and use cases. Essential Skills Background: Degree in computer science, robotics, electrical engineering, or a closely related field, or an equivalent combination of research and professional experience in robot learning or embodied AI. Robot Learning: Hands-on experience building or working with learning-based robot policies, including imitation learning, visuomotor control, VLA models, or closely related embodied AI methods. Robotics Systems: Practical experience with robotic hardware, including real robot manipulation, ROS/ROS2, sensor integration, control interfaces, or real-world robot deployment. ML Engineering: Working proficiency in Python and PyTorch, including the ability to implement models, training loops, data loaders, and evaluation pipelines beyond notebook-level prototypes. Experimental Practice: Ability to design experiments, compare model variants, debug failures across the ML/robotics boundary, and iterate toward measurable performance on real tasks. Beneficial Skills VLA Experience: Familiarity with VLA models or robotic foundation models such as π0, GR00T, or equivalent, including action-token or action-chunk representations. Policy Deployment: Experience deploying learned models on physical robots or edge compute platforms, including latency-constrained inference pipelines. Reinforcement Learning: Experience with online or offline RL for robotics, including reward design, simulation-based training, or combining RL with imitation learning. Simulation: Experience with simulators such as MuJoCo, Isaac Sim, or ManiSkill and…